Mastering Commercial Market Predictions: A Stacked Ensemble Approach with Credit Scoring Models

Commercial market predictions play a pivotal role in strategic decision-making for businesses, investors, and financial institutions. Accurate predictions enable proactive responses to market dynamics, reducing risks and maximizing opportunities. This paper introduces a novel approach to enhance commercial market predictions by leveraging a stacked ensemble learning techniques and a credit scoring model. The fundamental goal of this study is to generate a strong prediction model that capitalises on the extensive body of data available in financial markets, including credit history, payment behavior, and financial transactions. To achieve this, the research integrates stacked ensemble learning, which combines multiple predictive models to mitigate individual weaknesses and enhance overall performance using different weights of ensemble model. The stacked ensemble learning framework incorporates diverse machine learning models which includes base model as Random Forest (RF), Naïve Bayes (NB), Logistic Regression (LR) and Meta model as LightGBM (Light Gradient Boosting Machine) for commercial market predictions. This research presents a paradigm shift in commercial market predictions, capitalizing on the wealth of financial data and the collective strength of ensemble learning techniques. Based on the Benchmark German Credit dataset, the proposed model is validated. During the tests, the suggested model attained a highest classification accuracy of 97.1%.